Small Cell Lung Cancer

El Mehdi Baknine, s194533

Jakob Frostholm Højgaard, s194527

Jonathan Dragestad Møller, s184243

Mikkel Niklas Rasmussen, s193518

Thomas Malthe Mølgaard Tams, s204540

2023-11-28

Introduction

Paper and data source

Title: Comprehensive genomic profiles of small cell lung cancer, George J. et. al. (2015)

Loading:
- Dim: 81 x 31669
- 30 metadata
- 31639 gene expression

Data clean:
- Check duplicate IDs
- Clean weird variables
- Check NAs

Data augment:
- 33 metadata
- 400 transcripts

Purpose: Identify different small cell lung cancer profiles

Methods

Load, clean and augment

Load in data from two different sheets in an excel file and combine these into a single file

Clean the data by creating usable column names and check that NAs exists

Augment 3 new variables:
Survival status - Dead/alive - Treatment type

Methods

Analysis specific methods

Select transcripts of interest via Kmeans clustering

Heatmapping of expression values - Data exploration

Hierarchical clustering of samples - Two groups

PCA - Check metadata and identify possible transcripts

Logistic regression - Statistically identify transcripts of interest in each group

Overview of metadata

Overview of metadata

Results

Results

Results

Conclusion

Confirmation of Study Findings

Data Format Consideration

Inclusion of Healthy Controls

Network Biology Analysis